Summary of Model Agnostic Hybrid Sharding For Heterogeneous Distributed Inference, by Claudio Angione et al.
Model Agnostic Hybrid Sharding For Heterogeneous Distributed Inference
by Claudio Angione, Yue Zhao, Harry Yang, Ahmad Farhan, Fielding Johnston, James Buban, Patrick Colangelo
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Cryptography and Security (cs.CR); Distributed, Parallel, and Cluster Computing (cs.DC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed sharding framework, Nesa, addresses challenges in data privacy, computational resources, and accessibility for large-scale AI models. It enables efficient distributed training and inference of recent models even on consumer-grade hardware through blockchain-based sequential deep neural network sharding. The framework uses personalized heuristics and routing mechanisms to distribute tasks across a diverse network of nodes. Compression techniques like dynamic blockwise quantization and mixed matrix decomposition reduce data transfer and memory needs, while robust security measures ensure data integrity and confidentiality using trusted execution environments. Evaluations across NLP and vision tasks show that these compression strategies do not compromise model accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Nesa is a new way to make large AI models work together without needing powerful computers or sensitive data. It helps keep information safe by spreading the work out among many smaller machines, like a puzzle with many pieces. This makes it easier for people to use these big AI models, even if they don’t have super-powerful computers. The system also uses special techniques to make the data transfer faster and more efficient. |
Keywords
» Artificial intelligence » Inference » Neural network » Nlp » Quantization
